Through-the-Wall Radar Human Activity Micro-Doppler Signature Representation Method Based on Joint Boulic-Sinusoidal Pendulum Model

TL;DR

Through-the-wall HAR suffers from feature redundancy and poor generalization when using traditional RTM/DTM representations. This work introduces a physics-based micro-Doppler corner representation built on a joint Boulic-Sinusoidal Pendulum model, reducing Doppler and range information to a compact set of $30$ corners on $R^2TM$ and $22$ corners on $D^2TM$ (static cases use $30$ for both), and a two-stage eight-node motion model that captures limb dynamics. The approach yields a PC-RD ($60\times3$) corner point cloud via SOGGDD-based extraction and demonstrates improved separability, robustness, and cross-tester/wall generalization in both simulated and measured data. These results suggest a path toward interpretable, generalizable HAR for indoor activities in realistic through-the-wall scenarios, with potential for more reliable deployment across diverse users and environments.

Abstract

With the help of micro-Doppler signature, ultra-wideband (UWB) through-the-wall radar (TWR) enables the reconstruction of range and velocity information of limb nodes to accurately identify indoor human activities. However, existing methods are usually trained and validated directly using range-time maps (RTM) and Doppler-time maps (DTM), which have high feature redundancy and poor generalization ability. In order to solve this problem, this paper proposes a human activity micro-Doppler signature representation method based on joint Boulic-sinusoidal pendulum motion model. In detail, this paper presents a simplified joint Boulic-sinusoidal pendulum human motion model by taking head, torso, both hands and feet into consideration improved from Boulic-Thalmann kinematic model. The paper also calculates the minimum number of key points needed to describe the Doppler and micro-Doppler information sufficiently. Both numerical simulations and experiments are conducted to verify the effectiveness. The results demonstrate that the proposed number of key points of micro-Doppler signature can precisely represent the indoor human limb node motion characteristics, and substantially improve the generalization capability of the existing methods for different testers.

Figures (14)

• Figure 1: The logic of the construction of the proposed theoretical work and the structure of the paper.
• Figure 2: Schematic diagram of the proposed joint Boulic-sinusoidal pendulum model.
• Figure 3: Principle of LFMCW signal transmitting, receiving, sampling, and time-frequency analysis.
• Figure 4: Schematic diagrams of the experimental scenarios: (a) through-the-wall, simulation, and (b) through-the-wall, real-world measurement. The motion captured data in (a) comes from open source works, while (b) is from the prototype TWR system we built ourselves.
• Figure 5: Schematic diagram of the generation process of $\mathbf{PC-RD}$.